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  • Research Article
  • Open Access

Note Onset Detection via Nonnegative Factorization of Magnitude Spectrum

EURASIP Journal on Advances in Signal Processing20082008:231367

  • Received: 6 November 2007
  • Accepted: 6 May 2008
  • Published:


A novel approach for onset detection of musical notes from audio signals is presented. In contrast to most commonly used conventional approaches, the proposed method features new detection functions constructed from the linear temporal bases that are obtained from the decomposition of musical spectra using nonnegative matrix factorization (NMF). Three forms of detection function, namely, first-order difference function, psychoacoustically motivated relative difference function, and constant-balanced relative difference function, are considered. As the approach works directly on input data, no prior knowledge or statistical information is therefore required. Practical issues, including the choice of the factorization rank and detection robustness to instruments, are also examined experimentally. Due to the scalability issue with the generated nonnegative matrix, the proposed method is only applied to relatively short, single instrument (or voice) recordings. Numerical examples are provided to show the good performance of the proposed method, including comparisons between the three detection functions.


  • Practical Issue
  • Audio Signal
  • Difference Function
  • Full Article
  • Temporal Basis

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Authors’ Affiliations

Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, GU2 7XH, United Kingdom
Samsung Electronics Research Institute, Communication House, Staines, TW18 4QE, United Kingdom
Winton Capital Management Ltd, London, W8 6LS, United Kingdom
Advanced Signal Processing Research Group, Department of Electronic and Electrical Engineering, Loughborough University, Loughborough, Leics, LE11 3TU, United Kingdom
Centre of Digital Signal Processing, Cardiff University, Cardiff, CF24 3AA, United Kingdom


© WenwuWang et al. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.